# Copyright 2023 Huawei Technologies Co., Ltd
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

import sys
import os
import json
import glob
import statistics

path_pattern = sys.argv[1]
target_type = sys.argv[2]
best_value, best_result, best_name = None, None, None
mean_result = {}
print(path_pattern)
for dir_path in glob.glob(path_pattern, recursive=True):
    entry = os.path.basename(dir_path)
    valid_result = None
    test_found = os.path.exists(os.path.join(dir_path, "test_results.json"))
    valid_path = os.path.join(dir_path, "results.json")
    if os.path.exists(valid_path):
        print(entry)
        with open(valid_path) as file:
            valid_result = json.load(file)
    else:
        print(f"{entry} no validation results")
        continue
    if not test_found:
        print(f"{entry} not tested yet")
    if target_type == "max":
        metric = sys.argv[3]
        metric_value = valid_result[metric]
        if best_value is None or metric_value > best_value:
            best_value = metric_value
            best_result = valid_result
            best_name = entry
    elif target_type == "mean" or target_type == "median":
        if mean_result:
            for metric, value in valid_result.items():
                if metric not in ["type", "epoch"]:
                    mean_result[metric].append(value)
        else:
            mean_result = {metric: [value] for metric, value in valid_result.items() if
                           metric not in ["type", "epoch"]}

if target_type == "max":
    print(f"Best result found at {best_name}: {best_result}")
elif target_type == "mean":
    mean_result = {metric: sum(value) / len(value) for metric, value in mean_result.items()}
    print(f"Mean result {mean_result}")
elif target_type == "median":
    mean_result = {metric: statistics.median(value) for metric, value in mean_result.items()}
    print(f"Mean result {mean_result}")
